As a rising product-management executive prepares for an important presentation to her firm’s senior team, she notices that something looks off in the market share numbers. She immediately asks her assistant to verify the figures. He digs in and finds an error in the data supplied by the market research department, and the executive makes the necessary corrections. Disaster averted! The presentation goes very well, and the executive is so delighted that she makes an on-the-spot award to her assistant. She concludes, “You know, we should make it a policy to double-check these numbers every time.” No one thinks to inform the people in Market Research of the error, much less work with the group to make sure that the proper data is supplied the next time.
Data’s Credibility Problem
Reprint: R1312E
Fifty years after the expression “garbage in, garbage out” was coined, we still struggle with data quality. Studies show that knowledge workers waste a significant amount of time looking for data, identifying and correcting errors, and seeking confirmatory sources for data they do not trust. When data are unreliable, managers quickly lose faith in them and fall back on their intuition to make decisions, steer their companies, and implement strategy. They’re also much more apt to reject important, counterintuitive implications that emerge from big data analyses.
But improving data quality is often not as hard as you might think, the author asserts. The solution is not better technology. It lies in better communication between the creators of data and the data users; a focus on getting the process right going forward rather than on cleaning up existing bad data; and, above all, the shifting of responsibility for data quality away from IT folks, who don’t own the business processes that create the data, and into the hands of managers, who are highly invested in getting the data right.
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